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    <title>MaplePrimes - Newest Questions</title>
    <link>http://www.mapleprimes.com/questions</link>
    <language>en-us</language>
    <copyright>2026 Maplesoft, A Division of Waterloo Maple Inc.</copyright>
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    <lastBuildDate>Mon, 31 Aug 2026 18:36:20 GMT</lastBuildDate>
    <pubDate>Mon, 31 Aug 2026 18:36:20 GMT</pubDate>
    <itunes:subtitle />
    <itunes:summary />
    <description>The questions most recently asked on MaplePrimes</description>
    <image>
      <url>http://www.mapleprimes.com/images/mapleprimeswhite.jpg</url>
      <title>MaplePrimes - Newest Questions</title>
      <link>http://www.mapleprimes.com/questions</link>
    </image>
    <item>
      <title>dsolve series, number of terms?</title>
      <link>http://www.mapleprimes.com/questions/243758-Dsolve-Series-Number-Of-Terms?ref=Feed:MaplePrimes:New%20Questions</link>
      <itunes:summary>&lt;p&gt;I would like to solve a differential equation using a series expansion in the attached file for practice purposes. How can the number of terms in the series solution be adjusted? I couldn&amp;#39;t find anything about this in the help text.&lt;br&gt;
BTW:&lt;br&gt;
The differential equation is exceptionally tricky for a symbolic solution ;-) .&lt;/p&gt;

&lt;p&gt;&lt;a href="/view.aspx?sf=243758_question/testdgl-1.mw"&gt;testdgl-1.mw&lt;/a&gt;&lt;/p&gt;
</itunes:summary>
      <description>&lt;p&gt;I would like to solve a differential equation using a series expansion in the attached file for practice purposes. How can the number of terms in the series solution be adjusted? I couldn&amp;#39;t find anything about this in the help text.&lt;br&gt;
BTW:&lt;br&gt;
The differential equation is exceptionally tricky for a symbolic solution ;-) .&lt;/p&gt;

&lt;p&gt;&lt;a href="/view.aspx?sf=243758_question/testdgl-1.mw"&gt;testdgl-1.mw&lt;/a&gt;&lt;/p&gt;
</description>
      <guid>243758</guid>
      <pubDate>Mon, 31 Aug 2026 08:46:20 Z</pubDate>
      <itunes:author>Alfred_F</itunes:author>
      <author>Alfred_F</author>
    </item>
    <item>
      <title>Drawing planes and points</title>
      <link>http://www.mapleprimes.com/questions/243757-Drawing-Planes-And-Points?ref=Feed:MaplePrimes:New%20Questions</link>
      <itunes:summary>&lt;p&gt;Dear Maple users&lt;/p&gt;

&lt;p&gt;&lt;span class="HwtZe"&gt;&lt;span class="jCAhz ChMk0b"&gt;&lt;span class="ryNqvb"&gt;I wanted Maple to draw two planes in 3D with some points on each plane. Each plane are given by an equation of the form a*x+b*y+c*z+d=0.&amp;nbsp;I know there are a number of options. &lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;

&lt;p&gt;&lt;span class="HwtZe"&gt;1. i&lt;span class="jCAhz ChMk0b"&gt;&lt;span class="ryNqvb"&gt;mplicitplot3d.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;

&lt;p&gt;&lt;span class="HwtZe"&gt;&lt;span class="jCAhz ChMk0b"&gt;&lt;span class="ryNqvb"&gt;2. Isolate z in the equation in order to&amp;nbsp;plot the plane as the graph of a function of two variables using plot3d.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;

&lt;p&gt;&lt;span class="HwtZe"&gt;&lt;span class="jCAhz ChMk0b"&gt;&lt;span class="ryNqvb"&gt;3. Find two vectors orthogonal to the normal vector and create a plot of a parametrized surface using plot3d.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;

&lt;p&gt;&lt;span class="HwtZe"&gt;&lt;span class="jCAhz ChMk0b"&gt;&lt;span class="ryNqvb"&gt;4. Use the command PlanePlot from the Student:-LinearAlgebra package.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;

&lt;p&gt;&lt;span class="HwtZe"&gt;The first option delivers an ugly result due to the grid. It also requires quite a lot of computation internally. The second option can be used, but one need to isolate z in the equation (and what if the plane has the equation z = constant). Option three also is OK, but again require some calculations. The fourth option I just found and it is quite interesting. Unfortunately my points did fall beyond the small square representing the plane, and it seems like there is no way to enlarge this square.&lt;/span&gt;&lt;/p&gt;

&lt;p&gt;&lt;span class="HwtZe"&gt;I ended up choosing option 2 and put some transparency on each plane. I am just puzzled why it is so&amp;nbsp;&lt;/span&gt;laborious to plot those planes and points. GeoGebra could do it right away.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;Or maybe I have overlooked something?&lt;/p&gt;

&lt;p&gt;KInd regards,&lt;/p&gt;

&lt;p&gt;Erik&lt;/p&gt;
</itunes:summary>
      <description>&lt;p&gt;Dear Maple users&lt;/p&gt;

&lt;p&gt;&lt;span class="HwtZe" jsaction="mouseup:Sxi9L,BR6jm; mousedown:qjlr0e" jsname="jqKxS" lang="en"&gt;&lt;span class="jCAhz ChMk0b" jsaction="agoMJf:PFBcW;MZfLnc:P7O7bd;nt4Alf:pvnm0e,pfE8Hb,PFBcW;B01qod:dJXsye;H1e5u:iXtTIf;lYIUJf:hij5Wb;tSpjdb:qAKMYb" jscontroller="BiTO4b" jsname="txFAF"&gt;&lt;span class="ryNqvb" jsaction="click:PDNqTc,GFf3ac,qlVvte;contextmenu:Nqw7Te,QP7LD; mouseout:Nqw7Te; mouseover:PDNqTc,c2aHje" jsname="W297wb"&gt;I wanted Maple to draw two planes in 3D with some points on each plane. Each plane are given by an equation of the form a*x+b*y+c*z+d=0.&amp;nbsp;I know there are a number of options. &lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;

&lt;p&gt;&lt;span class="HwtZe" jsaction="mouseup:Sxi9L,BR6jm; mousedown:qjlr0e" jsname="jqKxS" lang="en"&gt;1. i&lt;span class="jCAhz ChMk0b" jsaction="agoMJf:PFBcW;MZfLnc:P7O7bd;nt4Alf:pvnm0e,pfE8Hb,PFBcW;B01qod:dJXsye;H1e5u:iXtTIf;lYIUJf:hij5Wb;tSpjdb:qAKMYb" jscontroller="BiTO4b" jsname="txFAF"&gt;&lt;span class="ryNqvb" jsaction="click:PDNqTc,GFf3ac,qlVvte;contextmenu:Nqw7Te,QP7LD; mouseout:Nqw7Te; mouseover:PDNqTc,c2aHje" jsname="W297wb"&gt;mplicitplot3d.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;

&lt;p&gt;&lt;span class="HwtZe" jsaction="mouseup:Sxi9L,BR6jm; mousedown:qjlr0e" jsname="jqKxS" lang="en"&gt;&lt;span class="jCAhz ChMk0b" jsaction="agoMJf:PFBcW;MZfLnc:P7O7bd;nt4Alf:pvnm0e,pfE8Hb,PFBcW;B01qod:dJXsye;H1e5u:iXtTIf;lYIUJf:hij5Wb;tSpjdb:qAKMYb" jscontroller="BiTO4b" jsname="txFAF"&gt;&lt;span class="ryNqvb" jsaction="click:PDNqTc,GFf3ac,qlVvte;contextmenu:Nqw7Te,QP7LD; mouseout:Nqw7Te; mouseover:PDNqTc,c2aHje" jsname="W297wb"&gt;2. Isolate z in the equation in order to&amp;nbsp;plot the plane as the graph of a function of two variables using plot3d.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;

&lt;p&gt;&lt;span class="HwtZe" jsaction="mouseup:Sxi9L,BR6jm; mousedown:qjlr0e" jsname="jqKxS" lang="en"&gt;&lt;span class="jCAhz ChMk0b" jsaction="agoMJf:PFBcW;MZfLnc:P7O7bd;nt4Alf:pvnm0e,pfE8Hb,PFBcW;B01qod:dJXsye;H1e5u:iXtTIf;lYIUJf:hij5Wb;tSpjdb:qAKMYb" jscontroller="BiTO4b" jsname="txFAF"&gt;&lt;span class="ryNqvb" jsaction="click:PDNqTc,GFf3ac,qlVvte;contextmenu:Nqw7Te,QP7LD; mouseout:Nqw7Te; mouseover:PDNqTc,c2aHje" jsname="W297wb"&gt;3. Find two vectors orthogonal to the normal vector and create a plot of a parametrized surface using plot3d.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;

&lt;p&gt;&lt;span class="HwtZe" jsaction="mouseup:Sxi9L,BR6jm; mousedown:qjlr0e" jsname="jqKxS" lang="en"&gt;&lt;span class="jCAhz ChMk0b" jsaction="agoMJf:PFBcW;MZfLnc:P7O7bd;nt4Alf:pvnm0e,pfE8Hb,PFBcW;B01qod:dJXsye;H1e5u:iXtTIf;lYIUJf:hij5Wb;tSpjdb:qAKMYb" jscontroller="BiTO4b" jsname="txFAF"&gt;&lt;span class="ryNqvb" jsaction="click:PDNqTc,GFf3ac,qlVvte;contextmenu:Nqw7Te,QP7LD; mouseout:Nqw7Te; mouseover:PDNqTc,c2aHje" jsname="W297wb"&gt;4. Use the command PlanePlot from the Student:-LinearAlgebra package.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;

&lt;p&gt;&lt;span class="HwtZe" jsaction="mouseup:Sxi9L,BR6jm; mousedown:qjlr0e" jsname="jqKxS" lang="en"&gt;The first option delivers an ugly result due to the grid. It also requires quite a lot of computation internally. The second option can be used, but one need to isolate z in the equation (and what if the plane has the equation z = constant). Option three also is OK, but again require some calculations. The fourth option I just found and it is quite interesting. Unfortunately my points did fall beyond the small square representing the plane, and it seems like there is no way to enlarge this square.&lt;/span&gt;&lt;/p&gt;

&lt;p&gt;&lt;span class="HwtZe" jsaction="mouseup:Sxi9L,BR6jm; mousedown:qjlr0e" jsname="jqKxS" lang="en"&gt;I ended up choosing option 2 and put some transparency on each plane. I am just puzzled why it is so&amp;nbsp;&lt;/span&gt;laborious to plot those planes and points. GeoGebra could do it right away.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;Or maybe I have overlooked something?&lt;/p&gt;

&lt;p&gt;KInd regards,&lt;/p&gt;

&lt;p&gt;Erik&lt;/p&gt;
</description>
      <guid>243757</guid>
      <pubDate>Sat, 29 Aug 2026 21:56:03 Z</pubDate>
      <itunes:author>erik10</itunes:author>
      <author>erik10</author>
    </item>
    <item>
      <title>Solver takes hours to return</title>
      <link>http://www.mapleprimes.com/questions/243756-Solver-Takes-Hours-To-Return?ref=Feed:MaplePrimes:New%20Questions</link>
      <itunes:summary>&lt;p&gt;I have a very large system of nonlinear equations in Maple 2025, and the solver takes hours. Any Suggestions?&lt;/p&gt;
</itunes:summary>
      <description>&lt;p&gt;I have a very large system of nonlinear equations in Maple 2025, and the solver takes hours. Any Suggestions?&lt;/p&gt;
</description>
      <guid>243756</guid>
      <pubDate>Fri, 28 Aug 2026 04:49:55 Z</pubDate>
      <itunes:author>tommycoupe</itunes:author>
      <author>tommycoupe</author>
    </item>
    <item>
      <title>How to deal with this AI prompt error</title>
      <link>http://www.mapleprimes.com/questions/243755-How-To-Deal-With-This-AI-Prompt-Error?ref=Feed:MaplePrimes:New%20Questions</link>
      <itunes:summary>&lt;p&gt;After starting Maple&lt;/p&gt;

&lt;p&gt;&lt;img height="158" 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" width="470"&gt;&lt;/p&gt;

&lt;p&gt;I then tried simpler prompts like &amp;quot;Hello&amp;quot;, simplify(x),...&lt;/p&gt;

&lt;p&gt;For all prompts I get the same error message.&lt;br&gt;
I am not a frequent AI user but this is the first time I see that.&lt;br&gt;
Has anybody else experienced the same?&lt;/p&gt;

&lt;p&gt;Can someone send me a prompt that should work?&lt;/p&gt;

&lt;p&gt;(Model type was Simple.)&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;u&gt;Update:&lt;/u&gt;&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;It seems that this error occurs, when a second instance of Maple 2026.1 is started and AI is consulted in this instance.&amp;nbsp;&lt;/p&gt;
</itunes:summary>
      <description>&lt;p&gt;After starting Maple&lt;/p&gt;

&lt;p&gt;&lt;img height="158" 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" width="470" /&gt;&lt;/p&gt;

&lt;p&gt;I then tried simpler prompts like &amp;quot;Hello&amp;quot;, simplify(x),...&lt;/p&gt;

&lt;p&gt;For all prompts I get the same error message.&lt;br /&gt;
I am not a frequent AI user but this is the first time I see that.&lt;br /&gt;
Has anybody else experienced the same?&lt;/p&gt;

&lt;p&gt;Can someone send me a prompt that should work?&lt;/p&gt;

&lt;p&gt;(Model type was Simple.)&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;u&gt;Update:&lt;/u&gt;&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;It seems that this error occurs, when a second instance of Maple 2026.1 is started and AI is consulted in this instance.&amp;nbsp;&lt;/p&gt;
</description>
      <guid>243755</guid>
      <pubDate>Thu, 27 Aug 2026 19:54:59 Z</pubDate>
      <itunes:author>C_R</itunes:author>
      <author>C_R</author>
    </item>
    <item>
      <title>Why is D not efffective on (ln@cosh)</title>
      <link>http://www.mapleprimes.com/questions/243754-Why-Is-D-Not-Efffective-On-lncosh?ref=Feed:MaplePrimes:New%20Questions</link>
      <itunes:summary>&lt;p&gt;To&amp;nbsp;&lt;code&gt;D(ln@cosh)&lt;/code&gt; I have to add an argument to make it work (&lt;strong&gt;Edit&lt;/strong&gt;: no output returned):&lt;/p&gt;

&lt;pre class="prettyprint"&gt;
D(ln@cosh)(x);
                            sinh(x)
                            -------
                            cosh(x)
&lt;/pre&gt;

&lt;p&gt;According to &lt;a href='http://www.maplesoft.com/support/help/search.aspx?term=D' target='_new'&gt;?D&lt;/a&gt; I expected output according to this&lt;br&gt;
&lt;img src="data:image/png;base64,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"&gt;&lt;/p&gt;
</itunes:summary>
      <description>&lt;p&gt;To&amp;nbsp;&lt;code&gt;D(ln@cosh)&lt;/code&gt; I have to add an argument to make it work (&lt;strong&gt;Edit&lt;/strong&gt;: no output returned):&lt;/p&gt;

&lt;pre class="prettyprint"&gt;
D(ln@cosh)(x);
                            sinh(x)
                            -------
                            cosh(x)
&lt;/pre&gt;

&lt;p&gt;According to ?D I expected output according to this&lt;br /&gt;
&lt;img src="data:image/png;base64,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" /&gt;&lt;/p&gt;
</description>
      <guid>243754</guid>
      <pubDate>Thu, 27 Aug 2026 17:04:15 Z</pubDate>
      <itunes:author>C_R</itunes:author>
      <author>C_R</author>
    </item>
    <item>
      <title>Using simplify and implicitplot for ODE solutions</title>
      <link>http://www.mapleprimes.com/questions/243753-Using-Simplify-And-Implicitplot-For-ODE-Solutions?ref=Feed:MaplePrimes:New%20Questions</link>
      <itunes:summary>&lt;p&gt;In the attached file, I have solved an ordinary differential equation implicitly. The simplification that is obviously possible using &amp;quot;simplify&amp;quot; is not being performed. Furthermore, &amp;quot;implicitplot&amp;quot; is not working either. I would appreciate some advice.&lt;/p&gt;

&lt;p&gt;&lt;a href="/view.aspx?sf=243753_question/test.mw"&gt;test.mw&lt;/a&gt;&lt;/p&gt;
</itunes:summary>
      <description>&lt;p&gt;In the attached file, I have solved an ordinary differential equation implicitly. The simplification that is obviously possible using &amp;quot;simplify&amp;quot; is not being performed. Furthermore, &amp;quot;implicitplot&amp;quot; is not working either. I would appreciate some advice.&lt;/p&gt;

&lt;p&gt;&lt;a href="/view.aspx?sf=243753_question/test.mw"&gt;test.mw&lt;/a&gt;&lt;/p&gt;
</description>
      <guid>243753</guid>
      <pubDate>Thu, 27 Aug 2026 11:43:50 Z</pubDate>
      <itunes:author>Alfred_F</itunes:author>
      <author>Alfred_F</author>
    </item>
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